NRG Oncology Assessment of Artificial Intelligence Deep Learning-Based Auto-segmentation for Radiation Therapy: Current Developments, Clinical Considerations, and Future Directions.

Deep learning neural networks (DLNN) in Artificial intelligence (AI) have been extensively explored for automatic segmentation in radiotherapy (RT). In contrast to traditional model-based methods, data-driven AI-based models for auto-segmentation have shown high accuracy in early studies in research settings and controlled environment (single institution). Vendor-provided commercial AI models are made available as part of the integrated treatment planning system (TPS) or as a stand-alone tool that provides streamlined workflow interacting with the main TPS. These commercial tools have drawn clinics' attention thanks to their significant benefit in reducing the workload from manual contouring and shortening the duration of treatment planning. However, challenges occur when applying these commercial AI-based segmentation models to diverse clinical scenarios, particularly in uncontrolled environments. Contouring nomenclature and guideline standardization has been the main task undertaken by the NRG Oncology. AI auto-segmentation holds the potential clinical trial participants to reduce interobserver variations, nomenclature non-compliance, and contouring guideline deviations. Meanwhile, trial reviewers could use AI tools to verify contour accuracy and compliance of those submitted datasets. In recognizing the growing clinical utilization and potential of these commercial AI auto-segmentation tools, NRG Oncology has formed a working group to evaluate the clinical utilization and potential of commercial AI auto-segmentation tools. The group will assess in-house and commercially available AI models, evaluation metrics, clinical challenges, and limitations, as well as future developments in addressing these challenges. General recommendations are made in terms of the implementation of these commercial AI models, as well as precautions in recognizing the challenges and limitations.

Study on sewage characteristics in rural China and pollutants removal performance of biologically enhanced internal circulation treatment system.

With rapid economic development and urbanization in China, rural wastewater treatment has become an important issue. This study investigated 63 rural sewage treatment stations in northern, central and southern Shaanxi, China for a 1-year period, 2021 to 2022. The main purpose of the research was to investigate the quality and discharge characteristics of rural sewage, along with current problems in rural wastewater treatment, in order to provide evidence for the optimal construction and operation of rural sewage treatment stations. We found that the biodegradability of rural wastewater is adequate, and BOD5/COD ratio in sewage was 0.4, which is suitable for biological treatment. It has obvious intermittent flow cut-off characteristics, and the range of cut-off duration of sewage was 6-16 h/d, which leads to poor pollutant removal efficiency (COD: 50.0 ± 29.2%, NH4+-N: 46.0 ± 26.1%, TN: 38.5 ± 24.9% and TP: 38.3 ± 23.8%) in sewage treatment stations. In response to the above characteristics, the rural sewage biologically enhanced internal circulation treatment (BEICT) system was constructed. After 97 days of operation, the system has a stable removal effect on TN and TP with an average removal rate of 77.42% and 89.69%, respectively, under the condition of influent interruption for 12 h per day. The activated sludge of system maintained good activity and stable sedimentation performance during the whole experiment, with MLVSS/MLSS and SVI of 0.72 and 128 mL/g, respectively. This study can provide the basis and technical support for the accurate design of rural sewage treatment facilities, and has important significance for guiding the treatment of rural domestic sewage in China.

Tislelizumab Plus Chemotherapy Versus Placebo Plus Chemotherapy as First-Line Treatment for Chinese Patients with Advanced Esophageal Squamous Cell Carcinoma: A Cost-Effectiveness Analysis.

BACKGROUND AND OBJECTIVES: Advanced esophageal squamous cell carcinoma (ESCC) is a prevalent and highly malignant tumor with a poor prognosis. Recently, the RATIONALE-306 trial demonstrated that tislelizumab combined with chemotherapy provided overall survival benefits for these patients. This study aimed to assess the cost-effectiveness of this treatment approach in Chinese patients with advanced ESCC from the perspective of healthcare system. METHODS: A Markov model was constructed to assess the economic and health benefits associated with tislelizumab plus chemotherapy over a 10-year lifetime horizon, utilizing data from the RATIONALE-306 trial. The analysis encompassed the calculation of several key parameters, including the incremental cost-effectiveness ratio (ICER), total cost, incremental cost, total effectiveness, and incremental effectiveness. Tislelizumab was considered cost-effective if the ICER obtained was below the willingness-to-pay (WTP) threshold of US$38,223 per quality-adjusted life-year (QALY); otherwise, it would be deemed not cost-effective. To ensure the robustness of the findings, the results were subjected to one-way sensitivity analysis and probabilistic sensitivity analysis (PSA). RESULTS: In the base-case analysis, the incremental effectiveness and cost associated with tislelizumab plus chemotherapy, compared to chemotherapy alone, were determined to be 0.40 QALY and US$7604, respectively. This resulted in an ICER of US$18,846 per QALY, which is below the WTP threshold of US$38,223 per QALY. Furthermore, the results from the one-way sensitivity analysis and PSA indicated robustness of the findings. CONCLUSION: Our lifetime simulation study demonstrated that, in the case of advanced ESCC, the combination of tislelizumab and chemotherapy offers increased effectiveness compared to chemotherapy alone, albeit at a higher cost. Moreover, considering the current WTP threshold in China, the addition of tislelizumab to chemotherapy is considered a cost-effective approach.

First Report On Physician Assessment and Clinical Acceptability of Custom-Retrained Artificial Intelligence Models for Clinical Target Volume and Organs-at-Risk Auto-Delineation for Postprostatectomy Patients.

PURPOSE: To assess the clinical acceptability of a commercial deep-learning-based auto-segmentation (DLAS) prostate model that was retrained using institutional data for delineation of the clinical target volume (CTV) and organs-at-risk (OARs) for postprostatectomy patients, accounting for clinical and imaging protocol variations. METHODS AND MATERIALS: CTV and OARs of 109 prostate-bed patients were used to evaluate the performance of the vendor-trained model and custom retrained DLAS models using different training quantities. Two new models for OAR structures were retrained (n = 30, 60 data sets), while separate models were trained for a new CTV structure (n = 30, 60, 90 data sets), with the remaining data sets used for testing (n = 49, 19). The dice similarity coefficient (DSC), Hausdorff distance, and mean surface distance were evaluated. Six radiation oncologists performed a qualitative evaluation scoring both preference and clinical utility for blinded structure sets. Physician consensus data sets identified from the qualitative evaluation were used toward a separate CTV model. RESULTS: Both the 30- and 60-case retrained OAR models had median DSC values between 0.91 to 0.97, improving significantly over the vendor-trained model for all OARs except the penile bulb. The brand new 60-case CTV model had a median DSC of 0.70 improving significantly over the 30-case model. DLAS (60-case model) and manual contours were blinded and evaluated by physicians with contours deemed acceptable or precise for 87% and 94% of cases for DLAS and manual delineations, respectively. DLAS-generated CTVs were scored precise or acceptable in 54% of cases, compared with the manual delineation value of 73%. The 30-case physician consensus CTV model did not show a significant difference compared with the randomly selected models. CONCLUSIONS: Custom retraining using institutional data leads to performance improvement in the clinical utility and accuracy of DLAS for postprostatectomy patients. A small number of data sets are sufficient for building an institutional site-specific DLAS OAR model, as well as for training new structures. Data indicates the workload for identifying training data sets could be shared among groups for the male pelvic region, making it accessible to clinics of all sizes.

Hypofractionation and SABR: 25 years of evolution in medical physics and a glimpse of the future.

As we were invited to write an article for celebrating the 50th Anniversary of Medical Physics journal, on something historically significant, commemorative, and exciting happening in the past decades, the first idea came to our mind is the fascinating radiotherapy paradigm shift from conventional fractionation to hypofractionation and stereotactic ablative radiotherapy (SABR). It is historically and clinically significant since as we all know this RT treatment revolution not only reduces treatment duration for patients, but also improves tumor control and cancer treatment outcomes. It is also commemorative and exciting for us medical physicists since the technology development in medical physics has been the main driver for the success of this treatment regimen which requires high precision and accuracy throughout the entire treatment planning and delivery. This article provides an overview of the technological development and clinical trials evolvement in the past 25 years for hypofractionation and SABR, with an outlook to the future improvement.

Assessment of dose-volume histogram precision for five clinical systems.

PURPOSE: To investigate the dependency of dose-volume histogram (DVH) behavior and precision on underlying discretization using shapes and dose distributions with known analytical DVHs for five commercial DVH calculators. METHODS: DVHs and summary metrics were extracted from all five systems using synthetic cone and cylinder objects for which the true volume and DVH curves were known. Trends in the curves and metrics were explored by varying the underlying voxelization of the CT image, structure set, and dose grid as well by varying the geometry of the structure and direction of a linear dose gradient. Using synthetic structures allowed for comparison with ground truth DVH curves to assess their accuracy while an algorithm was additionally developed to assess the precision of each system. The precision was calculated with a novel algorithm that treats any "stair step" behavior in a DVH curve as an uncertainty band and calculates the width, characterized as a percent difference, of the band for various DVH metrics. The underlying voxelization was additionally changed and DVHs were extracted for two clinical examples. The details of how each system calculated DVHs were also investigated and tendencies in the calculated curves, metrics, and precision were related to choices made in the calculation methodology. RESULTS: Calculation methodology differences that had a noticeable impact on the DVH curves and summary metrics include supersampling beyond the input grids and interpretation of the superior and inferior ends of the structures. Among the systems studied, the median precision ranged from 0.902% to 3.22%, and interquartile ranges varied from 1.09% to 3.91%. CONCLUSIONS: Commercial dose-evaluation solutions can calculate different DVH curves, structure volume measures, and dose statistics for the same input data due to differences in their calculation methodologies. This study highlights the importance of understanding and investigating the DVH calculation when considering a new clinical system and when using more than one system for data transfer.

[Characteristic Analysis of SWAT Model Parameter Values Based on Assessment of Model Research Quality].

The soil and water assessment tool (SWAT) model is currently one of the most widely used watershed models in China. Since the model has been developed with distributed parameters and is customized to satisfy the environmental characteristics of the U.S.A., determining appropriate parameter values that reflect local features for model application in China is crucial. Some studies have proposed parameter values for the SWAT model by summarizing reported values in the literature; however, these studies neither differentiate the literature with respect to its quality nor consider non-uniformity in parameter values and the impact of extreme values. To address this, an indicator system for assessing the quality of SWAT model research was established, taking into account the process of model development, parameter calibration, and model validation as well as model performance. This screening approach was applied to a total of 428 journal articles on SWAT model research published between 2015 and 2017 were retrieved from the China National Knowledge Infrastructure database. The reported values of 15 model parameters involved in hydrology and sediment and nutrient simulation were extracted from highly credible articles and analysed in terms of statistical distributions, differences among geographic regions, and discrepancies between calibrated and default values. Results showed that the 129 highly credible journal articles screened generally followed good modelling practice and consisted of case studies from different regions across China. The statistical distributions of the 15 model parameters derived from the SWAT model studies exhibited a range of features including positive and negative skewness, and those of 4 parameters showed significant differences among regions where the watersheds are located. Furthermore, the calibrated values of 12 out of 15 parameters were significantly different from their default values. Considering the statistical characteristics of these model parameters, recommended parameter values for SWAT model application in China are proposed in the form of confidence intervals, and specific suggestions are also provided based on data availability.

Quantitative analysis of economic and environmental benefits for land fallowing policy in the Beijing-Tianjin-Hebei region.

The Beijing-Tianjin-Hebei region (BTH region) is a major crop-producing region of China suffering from environment deterioration. Land fallowing policy is widely used as the solution of agriculture-related environment pollution, while it is difficult to derive a quantitative basis for policy-making. An indicator system was established to quantitatively analyse economic and environmental benefits of land fallow policies in the BTH region. The system consisted of 8 indicators to describe the water, air and economic influences caused by land fallowing policy such as the amount of nutrient discharged, the emission of PM2.5-related and greenhouse gas, and the input and output of agriculture production. SWAT (Soil and Water Assessment Tool) and IAP-N (Improving Anthropogenic Practices of managing reactive Nitrogen) models were used to quantify environmental indicators. Five scenarios with different planting patterns and fallow intensities were simulated for five years with different hydrological conditions. It is found that the influence of policy on water environment is highly sensitive to hydrological conditions, while the influence on air environment and economy are more affected by different policy settings in scenarios. The marginal utilities of the fallowed area are indicator-dependent. Planting alternative crops can decrease production costs and keep crop yields, while may also cause atmospheric environmental pollution if the crops have nitrogen fixing ability. Indicators also have spatial and temporal heterogeneity under different planting patterns and fallow intensities. A policy evaluation and associated uncertainty analysis is essential for effective implementation. The analysis framework established could support decision-making in regions facing agri-environmental problems.

Technical Note: Vendor miscalibration of preclinical orthovoltage irradiator identified through independent output check.

PURPOSE: Accurate radiation dosimetry in radiobiological experiments is crucial for preclinical research in advancement of cancer treatment. Vendors of cell irradiators often perform calibration for end-users. However, calibration accuracy remains unclear due to missing detailed information on calibration equipment and procedures. In this study, we report our findings of a vender miscalibration of the radiation output and our investigation on the root cause of the discrepancy. METHODS: Independent calibration verification for a commercial preclinical orthovoltage irradiator was conducted. Initially, in the absence of ionization chambers calibrated at kV energy, radiochromic films (EBT3) was first calibrated at MV energy. Energy correction factors from literature were used to create an in-house kV dosimetry system. The miscalibration identified with the in-house kV EBT3 dosimetry was later confirmed by ADCL calibrated ionization chambers (Exradin A1SL and PTW 30013) at kV energy. Ionization chambers were suspended in-air following TG-61 recommendation for output calibration. To investigate the root cause of the miscalibration, additional measurements were performed with ionization chambers placed on the shelf. A validated Monte Carlo simulation code was also used to investigate the impact of placing the ionization chamber on the shelf instead of suspending it in air during the vendor-performed calibration process. RESULTS: Up to a 6% dosimetry error was observed when comparing the vendor calibrated output of the preclinical irradiator with our independent calibration check. Further investigation showed incorrect setups in the vendor's calibration procedure which may result in dose errors up to 11% from the backscatter of the shelf board during calibration, and up to 5% from omitting temperature and pressure corrections to ionization chamber readings. CONCLUSION: Our study revealed large dose calibration errors caused by incorrect setup and the omission of temperature/pressure correction in the vendor's calibration procedure. The findings also highlighted the importance of performing an independent check of the dose calibration for preclinical kV irradiators. More absolute dosimetry training is needed for both vendors and end users for establishing accurate absolute dosimetry.

Cone-beam computed tomography-based delta-radiomics for early response assessment in radiotherapy for locally advanced lung cancer.

Cone-beam computed tomography (CBCT) images acquired during radiotherapy may allow early response assessment. Previous studies have reported inconsistent findings on an association of CBCT-measured tumor volume changes with clinical outcomes. The purpose of this pilot study was twofold: (1) to characterize changes in CBCT-based radiomics features during treatment; and (2) to quantify the potential association of CBCT-based delta-radiomics features with overall survival in locally advanced lung cancer. We retrospectively identified 23 patients and calculated 658 radiomics features from each of 11 CBCT images per patient. Feature selection was performed based on repeatability, robustness against contouring uncertainties, and non-redundancy. We calculated the coefficient of determination (R 2) for the relationship between the actual feature value at the end of treatment and predicted value based on linear models fitted using features between the first and kth fractions. We also quantified the predictive ability for survival with two methods by: (1) comparing delta-radiomics features (defined as the mean change between the first and kth fractions) between two groups of patients divided by a cutoff survival time of 18 months using the t-test or Wilcoxon rank-sum test; and (2) quantifying univariate discrimination of two groups divided by the median of delta-radiomics feature. All selected seven radiomics features during treatment (as early as the 10th fraction) were predictive of those at the end of treatment (R 2 > 0.64). Three delta-radiomics features demonstrated significant differences (q < 0.05, as early as the 10th fraction) between the two groups of patients divided by the cutoff survival time. Two of those three features were also predictive of survival according to the log-rank statistics. We provided the first demonstration of a potential association of CBCT-based delta-radiomics features early during treatment with overall survival in locally advanced lung cancer. Our preliminary findings should be validated for a larger cohort of patients.

Radiomics for Response and Outcome Assessment for Non-Small Cell Lung Cancer.

Routine follow-up visits and radiographic imaging are required for outcome evaluation and tumor recurrence monitoring. Yet more personalized surveillance is required in order to sufficiently address the nature of heterogeneity in nonsmall cell lung cancer and possible recurrences upon completion of treatment. Radiomics, an emerging noninvasive technology using medical imaging analysis and data mining methodology, has been adopted to the area of cancer diagnostics in recent years. Its potential application in response assessment for cancer treatment has also drawn considerable attention. Radiomics seeks to extract a large amount of valuable information from patients' medical images (both pretreatment and follow-up images) and quantitatively correlate image features with diagnostic and therapeutic outcomes. Radiomics relies on computers to identify and analyze vast amounts of quantitative image features that were previously overlooked, unmanageable, or failed to be identified (and recorded) by human eyes. The research area has been focusing on the predictive accuracy of pretreatment features for outcome and response and the early discovery of signs of tumor response, recurrence, distant metastasis, radiation-induced lung injury, death, and other outcomes, respectively. This review summarized the application of radiomics in response assessments in radiotherapy and chemotherapy for non-small cell lung cancer, including image acquisition/reconstruction, region of interest definition/segmentation, feature extraction, and feature selection and classification. The literature search for references of this article includes PubMed peer-reviewed publications over the last 10 years on the topics of radiomics, textural features, radiotherapy, chemotherapy, lung cancer, and response assessment. Summary tables of radiomics in response assessment and treatment outcome prediction in radiation oncology have been developed based on the comprehensive review of the literature.

A virtual source model for Monte Carlo simulation of helical tomotherapy.

The purpose of this study was to present a Monte Carlo (MC) simulation method based on a virtual source, jaw, and MLC model to calculate dose in patient for helical tomotherapy without the need of calculating phase-space files (PSFs). Current studies on the tomotherapy MC simulation adopt a full MC model, which includes extensive modeling of radiation source, primary and secondary jaws, and multileaf collimator (MLC). In the full MC model, PSFs need to be created at different scoring planes to facilitate the patient dose calculations. In the present work, the virtual source model (VSM) we established was based on the gold standard beam data of a tomotherapy unit, which can be exported from the treatment planning station (TPS). The TPS-generated sinograms were extracted from the archived patient XML (eXtensible Markup Language) files. The fluence map for the MC sampling was created by incorporating the percentage leaf open time (LOT) with leaf filter, jaw penumbra, and leaf latency contained from sinogram files. The VSM was validated for various geometry setups and clinical situations involving heterogeneous media and delivery quality assurance (DQA) cases. An agreement of < 1% was obtained between the measured and simulated results for percent depth doses (PDDs) and open beam profiles for all three jaw settings in the VSM commissioning. The accuracy of the VSM leaf filter model was verified in comparing the measured and simulated results for a Picket Fence pattern. An agreement of < 2% was achieved between the presented VSM and a published full MC model for heterogeneous phantoms. For complex clinical head and neck (HN) cases, the VSM-based MC simulation of DQA plans agreed with the film measurement with 98% of planar dose pixels passing on the 2%/2 mm gamma criteria. For patient treatment plans, results showed comparable dose-volume histograms (DVHs) for planning target volumes (PTVs) and organs at risk (OARs). Deviations observed in this study were consistent with literature. The VSM-based MC simulation approach can be feasibly built from the gold standard beam model of a tomotherapy unit. The accuracy of the VSM was validated against measurements in homogeneous media, as well as published full MC model in heterogeneous media.

Commissioning and comprehensive evaluation of the ArcCHECK cylindrical diode array for VMAT pretreatment delivery QA.

We present commissioning and comprehensive evaluation for ArcCHECK as a QA equipment for volumetric-modulated arc therapy (VMAT), using the 6 MV photon beam with and without the flattening filter, and the SNC patient software (version 6.2). In addition to commissioning involving absolute dose calibration, array calibration, and PMMA density verification, ArcCHECK was evaluated for its response dependency on linac dose rate, instantaneous dose rate, radiation field size, beam angle, and couch insertion. Scatter dose characterization, consistency and symmetry of response, and dosimetry accuracy evaluation for fixed aperture arcs and clinical VMAT patient plans were also investigated. All the evaluation tests were performed with the central plug inserted and the homogeneous PMMA density value. Results of gamma analysis demonstrated an overall agreement between ArcCHECK-measured and TPS-calculated reference doses. The diode based field size dependency was found to be within 0.5% of the reference. The dose rate-based dependency was well within 1% of the TPS reference, and the angular dependency was found to be ± 3% of the reference, as tested for BEV angles, for both beams. Dosimetry of fixed arcs, using both narrow and wide field widths, resulted in clinically acceptable global gamma passing rates on the 3%/3mm level and 10% threshold. Dosimetry of narrow arcs showed an improvement over published literature. The clinical VMAT cases demonstrated high level of dosimetry accuracy in gamma passing rates.

Exposure-response analysis and risk assessment for lung cancer in relationship to silica exposure: a 44-year cohort study of 34,018 workers.

Crystalline silica has been classified as a human carcinogen by the International Agency for Research on Cancer (Lyon, France); however, few previous studies have provided quantitative data on silica exposure, silicosis, and/or smoking. We investigated a cohort in China (in 1960-2003) of 34,018 workers without exposure to carcinogenic confounders. Cumulative silica exposure was estimated by linking a job-exposure matrix to work history. Cox proportional hazards model was used to conduct exposure-response analysis and risk assessment. During a mean 34.5-year follow-up, 546 lung cancer deaths were identified. Categorical analyses by quartiles of cumulative silica exposure (using a 25-year lag) yielded hazard ratios of 1.26, 1.54, 1.68, and 1.70, respectively, compared with the unexposed group. Monotonic exposure-response trends were observed among nonsilicotics (P for trend < 0.001). Analyses using splines showed similar trends. The joint effect of silica and smoking was more than additive and close to multiplicative. For workers exposed from ages 20 to 65 years at 0.1 mg/m(3) of silica exposure, the estimated excess lifetime risk (through age 75 years) was 0.51%. These findings confirm silica as a human carcinogen and suggest that current exposure limits in many countries might be insufficient to protect workers from lung cancer. They also indicate that smoking cessation could help reduce lung cancer risk for silica-exposed individuals.